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This work addresses the problem of finding boundary points in multidimensional data sets. Boundary points are data points that are located at the margin of densely distributed data such as a cluster. We describe a novel approach called BORDER (a BOundaRy points DEtectoR) to detect such points. BORDER employs the state-of-the-art database technique—the Gorder kNN join and makes use of the special property of the reverse k nearest neighbor (RkNN). Experimental studies on data sets with varying characteristics indicate that BORDER is able to detect the boundary points effectively and efficiently.
Boundary points, kNN join, k-nearest neighbor, reverse k-nearest neighbor.

B. C. Ooi, W. Hsu, C. Xia and M. L. Lee, "BORDER: Efficient Computation of Boundary Points," in IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 289-303, 2006.
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